Character recognition

Abstract: This invention relates to a method and apparatus for recognizing a script written character. The character is entered using character entering means and digitised by appropriate means. The digitised character is then stored in, for example, a memory. Codes representing topological vector features of the character are extracted from the character, then the topological and vector features of the character are compared with a set of reference topological and vector features stored in a memory. Each of the set of reference characters corresponding with a specific script written character. A logic process is then performed to determine which of the set of reference features most closely corresponds to the topological features of the digitized character thereby identifying the script written character. The relative weighting of the topological effect and the vector effect can be varied for different types of script to enable still more accurate recognition.

Description: This invention relates to a method and apparatus for character recognition, particularly the recognition of letters, numerals and other symbols such as Japanese characters, musical symbols, simple drawings and the like. In keyboardless input schemes for computer systems, for example, instructions or data can be input by the user of the system by means of a pen-like stylus on an input/output screen. An example of such a screen is a touch sensitive monitor. The input instructions or data can be handwritten script, numbers, or any other form of handwritten character. In these types of systems it is essential for the computer to "understand" the input it has been given by the user and to recognize and read the input characters. There are a number of known systems capable of recognizing and reading input characters. One such system is disclosed in US 4,972,496. This patent discloses a system including a transparent input screen that generates positional information corresponding to the positions at which a stylus contacts the screen (i.e. the positional information of the character) and also displays to the user the input that has been entered. The system further includes a computer which is programmed to compile the positional information into "Strokes" made by the stylus. The strokes are analyzed to identify the starting point of each stroke, the end of each stroke, the slope of the stroke, the stroke centriod, the rate of change of the slope, and where a character comprises more than one stroke whether it was the first, second, etc. Once the stroke alone has been analyzed a personalized data base which is unique to a specific user is accessed to identify the character which most closely corresponds to the character under analysis. This system is relatively complex to implement using dedicated hardware for stroke analysis due to the detailed stroke analysis which must be conducted. In addition it relies on a personalized data base for each user. This can mean that a large amount of memory is wasted if a given system is used by many users. If no personalized data base is set up the degree of recognition of the system can fall to an unacceptable level. Since this system relies only on stroke analysis it is important for all users to write the characters in a predetermined conventional manner. Recognition is severely inhibited when the user adopts a writing style where the strokes are different from the predetermined conventional manner. Even though the free style characters may look very similar to the conventional characters, recognition may not be accurate since the stroke analysis indicates a different character than that which has been written. Document DE-A- 3 882 671 discloses a method of and apparatus for comparing line characteristics or sets of line characteristics, especially to identify handwriting and signatures. To obtain a line characteristic the writing speed will be determined and afterwards the line will be divided into areas according to their different speeds. While comparing to signatures the corresponding previously stored characterizing data will be compared. Said characterizing data are intrinsic to a particular person. The amount of data to be processed decreases due to the classification into different speed areas or segments, thus making possible a real time processing. One object of the present invention is to provide a character recognition system which overcomes at least some of the disadvantages of known systems. Another object of the present invention is to provide a simple character recognition system which has improved recognition rate even when no personalized data base is utilized. A method and an apparatus for recognising a script written character are provided as set out in independent claims 1 and 13. The system of the invention has the advantages that there is no need for time consuming complex analysis of the character and that a data base will provide recognition of about upwards of 98% of all characters input by a user. Reference will now be made, by way of example, to the accompanying drawings, in which:- Figure 1 is a block diagram of a character recognition system according to one aspect of the present invention; Figure 2 is a diagram of an example of a program memory displayed on the screen of the system; Figure 3 is a diagram showing some examples of characters with frame constraint defaults; Figure 4 is a diagram of an STK format of the character A; Figure 5 is a diagram of a pattern format of the character A; Figure 6 is a diagram showing a topological feature set used to identify any character; and Figures 7 and 8 are diagrams for showing how the character is recognized. Referring to Figure 1 a character recognition system is shown generally at 10. The system comprises a micro-computer 12 having a monitor 14. The computer is connected to a touch sensitive screen, for example an LCD graphic digitizer 16. It is possible to replace the monitor with a touch sensitive monitor and dispense with the separate touch sensitive screen . A pen-like stylus 18 may be used on the touch sensitive screen to input instructions, data or the like to the computer. A user can write commands directly onto the screen. For the most accurate recognition of the commands the characters should preferably be entered in a frame shown generally at 20. The touch sensitive screen menu used by the user to enter information is shown in Figure 2. The main options available are shown in blocks 22 and may be activated by touching the stylus on the appropriate box 24a-f. The frame 20 is shown in greater detail in Figure 3. It comprises a top frame 26, a middle frame 28 and a bottom frame 30. Each character should be entered into one column 32. The position of the character is used to aid in the recognition of that character. For example "short" lower case letters such as a, c, e, m, n, o, r, s, u, v, w, x and z should be entered in the middle frame and should preferably be of such a size that they do not extend into the other frames. Taller lower case letters such as b, d, f, h, i, k, l, and t should have a larger vertical size than the smaller lower case letters and should be entered in the top and middle frames. Lower case letters with "tail" such as g, j, p, q and y should preferably be entered such that the tails extend into the bottom frame. Upper case letters and numbers should generally be entered in the top and middle frames of any given column 32. Other characters such as punctuation for example should be entered in their normal positions relative to the alphanumeric characters. The recognition of punctuation is not discussed in this document but is set out in our co-pending application (TI-17480). Adhering to the frame constraints illustrated above allows the system to recognize the difference between "c" and "C" for example. It is possible for the operator to set the size of the frames or to use the standard frame. In addition for those alphabets such as Japanese characters which do not have symbols of the same shape but different sizes; frame constraints are not necessary and the user can enter the characters in a box without any frame constraints. The same holds if the system is used for geometrical symbols or drawing recognition. The full detail of how the frame constraints aid recognition will be described in more detail below. Once the handwritten data has been entered on the LCD graphic digitizer 16 the output of the digitizer is downloaded to the computer into what is called a stroke format file (STK file). In such a format the character is described by a sequence of strokes each of which is made up of a series of coordinates between a pen-down and pen-up condition. Figure 4 shows an ASCII format translation of the content of a stroke file in binary form of the letter A. The output from the digitizer is then "pre-processed" before character recognition is implemented. This pre-processing comprises character scaling, centering, digital noise filtering and fitting the digital information into a pixel matrix of a given size, for example, a 16x16 matrix. After pre-processing the character undergoes a recognition process which comprises three basic steps: extraction of the topological characteristics (features) of the input handwritten character; character vector code determination; and recognition by 'fuzzy' comparison with a set of reference characters stored in a memory. The handwriting recognition is performed on each individual character once it has been written. This allows for total freedom in writing the characters according to the user's style, i.e. it is not necessary to enter the character as a particular sequence of strokes. This can allow for variations in the direction of movement of the pen and corrections can be effected after the characters have been originally written. The recognition is mainly based on symbol's optical characteristics. To solve ambiguities due to the optical aspect of the characters such as "S" and "5" a vector code is determined for each character. This vector code determination will be described in more detail below. As previously stated, the recognition of uppercase and lowercase letters with the same shape (e.g. 'c' and 'C') is improved by the user writing in fields or frames. This particular system is optimized for characters as well as all other signs usually found in a standard computer keyboard. However, the system has been optimised for all alphabets, Japanese characters, geometrical symbols and simple drawings. In addition the principle is valid for all other alphabets. The system can be used in a writer dependent mode, in which it shows better recognition rate. However, very good recognition is possible when used as a writer-independent system, i.e. without the system training. The user can enter his own reference character set through a user friendly character mode. Once adequately trained, the system can accept any handwriting style. For example, one user may train the system to recognize multiple character styles for a given alphabet letter, or his special way of writing a given character. Alternatively, the system can be used in "untrained mode" with a standard set of characters. The pre-processing initially converts the STK format described above to a pattern (PTN) format of a normalized 16x16 matrix. This PTN format is shown in Figure 5. The remainder of the character pre-processing is carried out as follows: Point Interpolation/Smoothing occurs as the digitiser converts points at a constant rate, the distance between two consecutive points is proportional to the pen speed. If the writing speed is low, some points can be very close to each other resulting in a digitising noise. Alternatively, if writing speed is relatively fast, points can be well spaced from each other, leaving holes in the stroke. To overcome this drawback, an interpolation/smoothing routine is used to add interpolated points whenever points are too far apart and remove points which are too close together; Character boundaries are evaluated and the points are scaled to fit into a normalized 16x16 matrix. X and Y components are scaled with different scaling factors to completely fill the matrix. If the ratio of either X-size/Y-size or Y-size/X-size is larger than a given threshold value (typically 4) only the larger component (X or Y) of the character is scaled. This is to avoid improper expansion of 'slim' characters such as "I" or "-"; and Character mapping which involves the number of points belonging to each of 16x16 pixels being counted and their average value computed. A threshold value (about 1/8 of the average value) is used to establish if a given pixel of the matrix is to be black (1) or white (0). If the number of points in a given pixel is greater than the threshold, the corresponding bit is set to 1, otherwise it is set to 0. Additional data are added to the character matrix to specify the character identifier (only available if the character is a member of the reference set), these data include the number of strokes of the character and the frame position. This information will be used during the recognition process, as will be described below. The frame position is coded by assigning a score to each of the three writing frames: 1 for the top frame 26, 2 for the central one 28, and 4 for the bottom one 30. For instance, character 'g' is normally written in the central and bottom frames, so its frame number will be 2+4=6. Frame numbers are evaluated by checking the character position on the writing field. In order to disregard small framing errors by the user, the active area of the top and bottom fields is actually slightly smaller than indicated by the frame lines. This means that the actual boundary between the central and top frame is slightly higher than shown and the boundary between the central and bottom frame is slightly lower than shown. The amount of this shifts may be adjusted by the user. The result of the character pre-processing stage is a 16x16 matrix in pattern format, each element of the matrix being a pixel of the character image. Allowed values for the given element are 0 (white pixel) and 1 (black pixel) as is shown in Figure 5. At the top of the matrix is a header data line containing the following information:- a) Character identifier (available only if the character is a member of the reference set); b) Number of strokes; and c) Frame positioning. The header data line may also contain character vector codes. The first step of the recognition process consists in extracting from the original 16x16 matrix and from the STK character format a topological and dynamic (vector) description or code of the characters. Recognition is then performed by comparing input character code with the codes of the reference characters collected by the user during the learning phase or from the standard set in the memory. The code basically contains four types of information: i) Feature extraction code; ii) Intersection code; iii) Frame position, stroke number and character identifier (the latter only being available if the character is a member of the reference set); and iv) Character vector code Feature extraction is the key step of the recognition algorithm. It performs a faithful description of character's topology to make recognition very accurate. This operation is performed by using a 'topological feature set'. It consists of the 99 16x16 matrices shown in figure 6 representing elementary geometrical structures. These matrices have been obtained through a complex work of optimization performed using several character sets from many different people. The feature extraction process basically consists of overlapping the character to the i-th (i=1,...,99) feature matrix and then counting the number of black pixels they have in common. This operation can be easily implemented by means of a bitwise AND function. The counting result X is then compared with three threshold values, T1, T2, T3; if X<=T1 the response fi of the i-th feature is set to 0 if T1<X<=T2 the response fi of the i-th feature is set to 1 if T2<X<=T3 the response fi of the i-th feature is set to 2 if X>=T3 the response fi of the i-th feature is set to 3 This process is shown in Figure 7. Therefore, the result of the described operation is an array of 99 integer values called feature extraction code representing the response of the feature set to the given character. T1, T2, T3 are chosen from simulations of the system and are typically set to be T1=2, T2=3, T3=6. The intersection code contains additional information on the character topology. It consists of 32 integer values Ni (i=1,...32), each of the them being the number of intersections of the character with the 16 horizontal and the 16 vertical lines of the matrix. An example of this operation is shown in Figure 8. The next field of the character code contains the frame position. the number of strokes of the character and, if the character is a member of the learning sets, its identifier, provided by the user. The last field is the character vector code information. This includes the following vector parameters: i) Nd, where Nd+1 is the number of points in the STK character format; ii) DIN, an array of Nd 4-bit values describing the position of each point in the STK character format respect to the previous one; iii) APPX, which is an array of Nd 3-bit values describing the position of the X-coordinate of each point of the STK character format with respect to the X-coordinate of the first, central and last points of the STK character format; iv) APPY, which is an array of Nd 3-bit values describing the position of the Y-coordinate of each point of the STK character format with respect to the Y-coordinate of the first, central and last points of the STK character format; and v) REL, which is an array of 12 2-bit values representing the relative position of the character extremal (max and min) points. The DIN parameter which describes the position of each point in the STK format respect to the previous one is determined in the following manner. Assume that (X1,Y1) are the co-ordinates of a given point in the STK format and that (X2,Y2) the co-ordinates of the subsequent one and so on. The pen-up and pen-down co-ordinates in the STK point sequence are disregarded, therefore the following method can be applied even if the two points belong to different strokes. The relative position of the two points are represented by using the following flow: if (X1 >X2) {if(Y1 > Y2) DIN=1 if(Y1 = Y2) DIN=2 if (Y1 < Y2) DIN=3} else if (X1 = X2) {if (Y1 > Y2) DIN=4 if (Y1 = Y2) DIN=5 if (Y1 < Y2) DIN=6} else if (X1 < X2) {if (Y1 > Y2) DIN=7 if (Y1 = Y2) DIN=9 if (Y1 < Y2) DIN=10} The number of DIN values for each character is Nd (number of points-1). Since the DIN values range between 1 and 10, a 4-bit value is needed to represent each of them. For instance, if a character is made up of the following 5 points: 1: (3,6) 2: (3,8) 3: (4,7) 4: (3,7) 5: (3,8) we obtain: Nd = (N.points -1) = 4; DIN[0] = 6; DIN[1] = 7; DIN[2] = 2; and DIN[3] = 6. The APPX and APPY parameters represent the position of each point in the STK character format respect to the first, central and last points. As previously indicated, pen-up and pen-down co-ordinates are not considered. Assuming: (X,Y) is the co-ordinates of the current point; (Xo,Yo) is the co-ordinates of the first point; (Xc,Yc) is the co-ordinates of the central point; and (Xn,Yn) is the co-ordinates of the last point. then APPX and APPY values are obtained as follows: if (Xc>X & Xn>X & Xo>X) APPX=0; if (Xc>X & Xn>X & XoX) APPX=1; if (Xc>X & XnX & Xo>X) APPX=2; if (Xc>X & XnX & XoX) APPX=3; if (XcX & Xn>X & Xo>X) APPX=4; if (XcX & Xn>X & XoX) APPX=5; if (XcX & XnX & Xo>X) APPX=6; and if (XcX & XnX & XoX) APPX=7 whilst if (Yc>Y & Yn>Y & Yo>Y) APPY=0; if (Yc>Y & Yn>Y & YoY) APPY=1; if (Yc>Y & YnY & Yo>Y) APPY= 2; if (Yc>Y & YnY & YoY) APPY=3; if (YcY & Yn>Y & Yo>Y) APPY=4; if (YcY & Yn>Y & YoY) APPY=5; if (YcY & YnY & Yo>Y) APPY=6; and if (YcY & YnY & YoY) APPY=7 The number of APPX and APPY values for each character is Nd, as for DIN parameters. Since the possible values range between 1 and 8 and a 3-bit value is needed to store the APPX/APPY code 2*3*Nd bits are required. For instance, the APP code for the character in the previous example is: APPX[0] = 3 APPX[1] = 7 APPX[2] = 3 APPX[3] = 3 APPY[0] = 7 APPY[1] = 5 APPY[2] = 3 APPY[3] = 6 where (Xo,Yo) = (3,6) (Xc,Yc) = (4,7) (Xn,Yn) = (3,8) The REL parameters which represent the relative position of the characters' extremal points are evaluated as follows: Assume that: Xmax is the maximum value of the X-co-ordinate in the STK character format; Ymax is the maximum value of the Y-co-ordinate in the STK character format; Xmin is the minimum value of the X-co-ordinate in the STK character format; and Ymin is the minimum value of the Y-co-ordinate in the STK character format; and Y(Xmax) is the y-co-ordinate of the point which Xmax belongs to; X(Ymax) is the x-co-ordinate of the point which Ymax belongs to; Y(Xmin) is the y-co-ordinate of the point which Xmin belongs to; and X(Ymin) is the x-co-ordinate of the point which Ymin belongs to. The 4 extremal points are considered in order of at the time at which they were written by the user and their co-ordinate values are compared. The result of the comparison is set to '+' if the difference is positive, to '-' if it is negative, to '=' if it is 0. For instance, if our example is considered, we get: Xmax=4 Ymax=8 Xmin=3 Ymin=6 and the time ordered sequence is: 1. (Xmin, Y(Xmin)) = (3,6) 2. (X(Ymin), Ymin) = (3,6) 3. (X(Ymax), Ymax) = (3,8) 4. (Xmax, Y(Xmax)) = (4,7) Comparing the 1st point with the 2nd we obtain: = = Comparing the 1st point with the 3rd we obtain: = - Comparing the 1st point with the 4th we obtain: - - Comparing the 2nd point with the 3rd we obtain: = - Comparing the 2nd point with the 4th we obtain: - - Comparing the 3rd point with the 4th we obtain: - - Therefore the REL code associated with the particular character is: '===---=-----' Since each symbol in the REL code can take 3 different values 4-bits are required to store each of them. So, the REL code of each character requires 2*12=24 bits. The DIN, APPX/APPY and REL codes, as well as the Nd values are added to the geometrical (or topological) character code described above. Therefore, the updated character code contains the following information: 1. Feature extraction code. 2. Intersection code. 3. Frame position, stroke number and character identifier (the latter only being available if the character is a member of the reference set). 4. Nd number. 5. DIN code. 6. APPX/APPY code. 7. REL code. Therefore, after the coding process a given character is represented by an array of integers: 99 from feature extraction, 32 from intersections, 1 for stroke number; 1 for frame position; 1 for Nd; Nd for DIN; 2Nd for APPX/APPY and 12 for REL. Recognition is performed by comparing value-by-value the code of the unknown input character with the coded characters in the reference set having the same frame number. Characters with frame numbers different from that of the input character are disregarded during the comparison. This has two important advantages. First, it allows recognition of characters with the same shape and different size. Secondly, the recognition process is relatively faster because it involves a lower number of comparisons. As a result, the score Sj is given by: Sj=Wo*Soj-Sdj and is assigned to the comparison between the unknown input character and the j-th character in the character database in the equation. In the equation Wo is a weighting factor which describes the relative importance of the optical and vector information; Soj is the so called optical or topographical score; and Sdj is the so called dynamic score. The "optical" score Soj assigned to the j-th comparisons (j=1,...,Ncar) is given by: where:- S = stroke number of the input character Stj = stroke number of the j-th reference character fi = response of the i-th feature to the input character fij = response of the i-th feature to the j-th reference character Ni = number of intersections of the i-th line with the input character Nij = number of intersections of the i-th line with the j-th reference character SW = stroke weight IW = intersection weight This score is evaluated for each of the Ncar reference characters. Sdj, the dynamical term of the character score can be expressed as the sum of three different terms: Sdj = Aj + Wr*Rj + Wp*¦ Nd-Ndj ¦ where Aj is the APP/DIN score; Rj is the REL score; and Wp amd Wr are weighting factors. Simulation results show that best values for weighting factors Wr and Wp are 8 and 4 respectively. The Aj score is evaluated by a comparison of the DIN and APPX/APPY codes. Let DIN, APPX, APPY and Nd the parameters refer to the input character and DINj, APPXj, APPYj and Ndj refer to the j-th character in the reference set. DIN, APPX, APPY are made up of Nd elements, DINj, APPXj, APPYj and Ndj contain Ndj elements. Since, in general, NdNdj and there is a high variability in handwriting, it is not sufficient to compare the DIN and APPX/APPY values as they are, but multiple comparisons are required, each of which has to be performed after shifting the two arrays one with respect to the other. Simulation results show that the best recognition rate is obtained considering 7 different relative shifts. For instance, if: The 7 shifts to be considered are: 1. DIN {1,3,2,7,5,9,5,8,4,3} DINj {3,4,3,3,2,7,6} APPX {4,4,6,5,3,7,6,6,4,5} APPXj {4,6,7,4,4,5,5} APPY {4,4,6,5,3,7,6,6,6,7} APPYj {2,5,6,3,4,7,6} 2. DIN {1,3,2,7,5,9,5,8,4,3} DINj {3,4,3,3,2,7,6} APPX {4,4,6,5,3,7,6,6,4,5} APPXj {4,6,7,4,4,5,5} APPY {4,4,6,5,3,7,6,6,6,7} APPYj {2,5,6,3,4,7,6} 3. DIN {1,3,2,7,5,9,5,8,4,3} DINj {3,4,3,3,2,7,6} APPX {4,4,6,5,3,7,6,6,4,5} APPXj {4,6,7,4,4,5,5} APPY {4,4,6,5,3,7,6,6,6,7} APPYj {2,5,6,3,4,7,6} 4. DIN {1,3,2,7,5,9,5,8,4,3} DINj {3,4,3,3,2,7,6} APPX {4,4,6,5,3,7,6,6,4,5} APPXj {4,6,7,4,4,5,5} APPY {4,4,6,5,3,7,6,6,6,7} APPYj {2,5,6,3,4,7,6} 5. DIN {1,3,2,7,5,9,5,8,4,3} DINj {3,4,3,3,2,7,6} APPX {4,4,6,5,3,7,6,6,4,5} APPXj {4,6,7,4,4,5,5} APPY {4,4,6,5,3,7,6,6,6,7} APPYj {2,5,6,3,4,7,6} 6. DIN {1,3,2,7,5,9,5,8,4,3} DINj {3,4,3,3,2,7,6} APPX {4,4,6,5,3,7,6,6,4,5} APPXj {4,6,7,4,4,5,5} APPY {4,4,6,5,3,7,6,6,6,7} APPYj {2,5,6,3,4,7,6} 7. DIN {1,3,2,7,5,9,5,8,4,3} DINj {3,4,3,3,2,7,6} APPX {4,4,6,5,3,7,6,6,4,5} APPXj {4,6,7,4,4,5,5} APPY {4,4,6,5,3,7,6,6,6,7} APPYj {2,5,6,3,4,7,6} For each of the 7 mentioned cases the comparison is performed as follows. APPX[i], that is i-th element of the APPX array is compared with APPXj[i] and APPY[i] is compared with APPYj[i]. The comparison is not performed if any of these elements is not available. Then, 3 different cases may occur: a) APPX[i]=APPXj[i] APPY[i]=APPYj[i] In this case the comparison result Cijk (k=1,...7) is given by the element located in the (DIN[i], DINj[i]) position of the matrix: b) APPX[i]APPXj[i] APPY[i]=APPYj[i] In this case the comparison result Cijk is given by the element located in the (DIN[i], DINj[i]) position of the matrix: c) APPX[i]=APPXj[i] APPY[i]APPYj[i] In this case the comparison result Cijk is given by the element located in the (DEN[i], DINj[i]) position of the matrix: d) APPX[i]APPXj[i] APPY[i]APPYj[i] The result of the comparison is Cijk=0. The overall result of the comparisons related to a given shift position is Cjk=SUM Cijk , where the sum has to be performed over all the i-values for which APPX(i) and APPXj(i) are simultaneously defined. Finally, the APP/DIN score is given by: Aj=max(Cjk)*1000/max(Nd, Ndj) k=1,..7 To evaluate the Rj score, the REL code of the input character and the RELj code of the j-th character in the reference set are compared value-by-value. Let RELi and RELji (i=1,...,12) represent the i-th value of REL and the RELj code, the Rj score is given by: where aij = 2 if RELi=RELij aij = 1 if RELiRELj and RELi or RLij is an '=' otherwise aij=0 For instance, if RELi = ' + = - - - = = + + + = = ' and RELji = '+ + + = = - - + = - - = ', Rj=13. Once Aj and Rj have been evaluated, they can be inserted to calculate Sdj. Then, the overall score Sj assigned to the comparison with the j-th character in the reference database is evaluated for each of the Ncar reference characters. The identifier of the reference character with the minimum score is assigned to the unknown input symbol. The role played by the dynamic weighting factors Wo, Wr, Wp and by the stroke weight SW is very important. In fact, the larger Wo is, the more important is the weight of the optical information in the recognition process. Wr and Wp describe the relative importance of the different components of the dynamical score Sdj. Another important parameter is the stroke weight SW: the larger it is, the larger is the influence of the stroke number. Therefore, depending on the application, it is possible to weight the relative importance of the optical and dynamical part of the algorithm by trimming the Wo, Wr, Wp and SW factors. For example, if the recognition algorithm is used for recognition of Japanese characters, which are generally written with the same stroke sequence, the dynamical contribution becomes very important and the Wo parameter should be smaller than that used for Roman characters. Likewise, SW, Wr and Wp should be larger. For some applications, this weighting factors can be trimmed by the user. Simulation results show that best values for weighting factors are Wo=24, Wr=8, Wp=4, SW=15 for Roman characters and Wo=20, Wr=8, Wp=4, SW=50 for Japanese characters. The learning process which may be used to educate the computer of the handwriting styles of the user is explained in more detail below. If the user does not wish to educate the system he may rely on recognition being effected by virtue of a reference set either permanently stored on the system or that of another user. The Learning process consists in collecting a suitable number of examples for each symbol the user wants to be recognized. These symbols will form the reference set during the recognition process. Two different learning (or training) modes are available. The first is known as the built-in learning mode. In this mode, the program requests the user to write examples of the symbols listed in a command file. This file contains a complete list of the symbols to be recognized with the corresponding frame constraints. The list of the symbols and the frame constraints can be changed by the user, depending upon the symbols he/she wants to be recognized and his/her handwriting style. Multiple frame constraint values are allowed for each symbol. In order to avoid insertion of bad characters in the learning set the program will reject characters with frame positions different from that stated in the file. The second mode is the interactive learning mode. The user writes symbols in the order he/she likes. Then recognition is performed. Each time the recogniser fails, unrecognized symbols are automaticaily inserted in the learning (or reference) set. Also, characters with not allowed frame positions will be rejected. After the built-in learning mode has been run, the recognition capability can be further improved by adding other characters to the learning set through the interactive learning mode. This HPCR (hand printed character recognition) project is capable of being implemented on hardware. To ease the migration to silicon, when necessary, the number of black pixels has been chosen constant and equal to 16. However this is merely a prefered choice and is not intended to be limiting. Migrating the above described system on silicon would provide several advantages, the most relevant being: An increase in Speed which would mean that dedicated hardware could exploit massive parallel architecture to perform the numerous comparison operations needed to implement both the feature extraction and the associative memory in a much shorter time; and The power consumption would be minimized and the dedicated hardware might be activated only when recognition has to be performed. Database memory could be integrated on chip. Thus saving the power needed to drive the external buses; and Dedicated chips may be employed in low-cost consumer products where powerful, state-of-the-art Micro chips are not available. It has to be mentioned that the system lends itself very well to silicon implementation. In fact, the operations to be performed, though numerous, are very simple in themselves (basically AND and EXNOR). The massively parallel architecture needed for high speed might be built around DRAM memory cores (used for database/feature storage) in a relatively simple way thanks to the internal structure of DRAM themselves. It should be noted that whilst the system is described with respect to characters forming part of one alphabet or another, recognition of other symbols can also be determined. For example, simple geometrical symbols can be identified by comparing their codes with those of reference geometrical symbols. Some examples of such symbols include semiconductor device symbols (i.e. resistors, capacitors, transistors, diodes, etc), musical symbols (notes, sharps, flats and the like), simple drawings (for example cats, dogs, sun, moon and many, many more). There is no limit to the scope of this system once a reference code has been generated for a given symbol.

Claims: A method for recognizing a script written character comprising the steps of entering the character using character enter means; digitizing the character; storing the digitized character; extracting topological features and vector features of said character; comparing the topological and vector features of the character with a set of reference topological and vector features stored in a memory, each of the set corresponding to a specific character; and performing a logic process to determine which of the set of reference features most closely corresponds to the topological and vector features of the digitized character, thereby recognizing the script written character, characterized in that said extraction step comprises extracting topological features and vector features of the character to be recognized, the extracting of vector features of the character including extracting a code, Nd, indicative of the number of points of the character, where Nd+ 1 is the number of points in a character format (STK) representative of a stroke format by representing the character as a sequence of strokes, each of the strokes being defined as a series of coordinates between a pen-down and a pen-up condition. The method of claim 1, characterized in that extracting topological and vector features comprises weighting the features of either the topology or the vector more than the other in a predetermined manner independent of the nature of the character. The method of claim 1 or claim 2, characterized in that extracting vector features comprises extracting features of the character in the form of one of more vector codes, each vector code being indicative of one of the vector features of the character. The method of any one of the preceding claims, characterized in that digitizing the character comprises constructing a matrix in ones and zeros and an array of points which are representative of the character. The method of any one of the preceding claims, characterized in that extracting the vector code further comprises extracting a code which describes the position of each point of the array relative to the previous point. The method of any one of the preceding claims, characterized in that said vector features are determined from dynamic features of how said character is formed. The method of any one of the preceding claims, characterized in that extracting the vector code further comprises extracting a code describing an X coordinate for each point with respect to one or more predetermined X coordinates of the character. The method of any one of the preceding claims, characterized in that extracting the vector code further comprises extracting a code describing a Y coordinate for each point with respect to one or more predetermined Y coordinates of the character. The method of any one of the preceding claims, characterized in that extracting the vector code further comprises extracting a code representing the relative positions of the extrernal points of the character. The method of any one of the preceding claims, wherein performing the logic process comprises evaluating a score Sj for each of the reference characters (Ncar) and identifying the reference character with the lowest score Sj as the character that was entered as the script written character, wherein Si is given by: Sj = Wo * Soj - Sdj where Wo is a weighting factor; Soj is a topological score; and Sdj is a vector score. The method of claim 10, wherein evaluation of said score Sj comprises evaluating Soj which is given by: where: S = stroke number of the input character Stj = stroke number of the j-th reference character fi = response of the i=th feature to the input character fij = response of the i=th feature to the j-th reference character Ni = number of intersections of the i-th line with the input character Nij= number of intersections of the i-th line with the j-th reference character SW= stroke weight IW = intersection weight. The method of claim 10, wherein evaluation of said score comprises evaluating Sdj which is given by: Sdj = Aj + Wr*Rj + Wp* | Nd-Ndj | where Aj is the APP/DIN score; Rj is the REL score; Wp and Wr are weighting factors; Nd is the (number of character points - 1) of the input character; and Ndj is the (number of character points - 1) of the j-th reference character. Apparatus for recognizing a script written character comprising: character enter means (16) for entering and receiving the character; digitizing means (18) for digitizing the character; storage means (12) for storing the digitized character; feature extracting means (12) for extracting topological features and vector features representative of said character; comparing means (12) for comparing the topological and vector features of the character with a set of reference topological and vector features stored in a memory, each of the set corresponding to a specific character; logic means (12) for identifying which of the set of reference features most closely corresponds to the topological features of the digitized character, thereby recognizing the script written character, characterized by means (12) for extracting topological features and vector features of the character to be recognized, the extracting of vector features of the character including extracting a code, Nd, indicative of the number of points of the character, where Nd+1 is the number of points in a character format (STK) representative of a stroke format by representing the character as a sequence of strokes, each of the strokes being defined as a series of coordinates between a pen-down and a pen-up condition. Apparatus according to claim 13, further including weighting means associated with the feature extracting means capable of giving a different weighting factor to each of said topological and vector features in a predetermined manner which is in dependence on the nature of the character. Apparatus according to claim 14, wherein the vector feature weighting factor is higher than the topological feature weighting factor for Japanese script. Apparatus according to claim 14, wherein the vector feature weighting factor is lower than the topological feature weighting factor for Roman script. Apparatus according to any one of claims 13 to 16, wherein the feature extracting means comprise code generating means for generating a plurality of codes indicative of the vector and topological features. Apparatus according to any one of claims 13 to 17, wherein the comparing means compares codes of the reference set and codes for the input character. Apparatus according to any one of claims 13 to 18, wherein the logic means evaluates a score Sj for each reference character (Nd) and identifies the reference character with the lowest score as that corresponding to the script written characters, wherein Sj is given by: Sj = Wo*Soj - Sdj where Wo is a weighting factor; Soj is a topological score; and Sdj is a vector score. Apparatus according to claim 19, wherein Soj is given by: where: S = stroke number of the input character Stj= stroke number of the j-th reference character fi = response of the i=th feature to the input character fij = response of the i=th feature to the j-th reference character Ni = number of intersections of the i-th line with the input character Nij= number of intersections of the i-th line with the j-th reference character SW= stroke weight IW = intersection weight Apparatus according to claim 19 or claim 20, wherein Sdj is given by: Sdj = Aj + Wr*Rj + Wp* | Nd-Ndj | where Aj is the APP/DIN score, Rj is the REL score; Wp and Wr are weighting factors; Nd is the (number of character points - 1) of the input character; and Ndj is the (number of character points - 1) of the j-th reference character. Apparatus according to any one of claims 13 to 21, wherein an interface is provided between the apparatus and a computer such that the interface passes the identified character to the computer thereby forming part of the instructions for the computer. A computer based product including apparatus according to any one of claims 13 to 21 and an interface between the apparatus and the computer for passing instruction to the computer from the apparatus.

Inventors: GALLO GIROLAMO, LATTARO CRISTINA, LUCENTINI FLAVIO, MAROTTA GIULIO, SAVARESE GIUSEPPE, GALLO, GIROLAMO, LATTARO, CRISTINA, LUCENTINI, FLAVIO, MAROTTA, GIULIO, SAVARESE, GIUSEPPE
Applicants: TEXAS INSTRUMENTS INC, TEXAS INSTRUMENTS INCORPORATED
Application year: 1994
Main classification: G06K9
IPC codes: G06K9